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Proceedings Paper

Multistage hierarchy for fast image analysis
Author(s): Maxim A. Grudin; David Mark Harvey; Leonid I. Timchenko
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Paper Abstract

In this paper, a novel approach is proposed, which allows for an efficient reduction of the amount of visual data required for representing structural information in the image. This is a multistage architecture which investigates partial correlations between structural image components. Mathematical description of the multistage hierarchical processing is provided, together with the network architecture. Initially the image is partitioned to be processed in parallel channels. In each channel, the structural components are transformed and subsequently separated, depending on their structural significance, to be then combined with the components from other channels for further processing. The output result is represented as a pattern vector, whose components are computed one at a time to allow the quickest possible response. The input gray- scale image is transformed before the processing begins, so that each pixel contains information about the spatial structure of its neighborhood. The most correlated information is extracted first, making the algorithm tolerant to minor structural changes.

Paper Details

Date Published: 17 December 1996
PDF: 9 pages
Proc. SPIE 2955, Image and Signal Processing for Remote Sensing III, (17 December 1996); doi: 10.1117/12.262880
Show Author Affiliations
Maxim A. Grudin, Liverpool John Moores Univ. (United Kingdom)
David Mark Harvey, Liverpool John Moores Univ. (United Kingdom)
Leonid I. Timchenko, Vinnitsa State Technical Univ. (Ukraine)

Published in SPIE Proceedings Vol. 2955:
Image and Signal Processing for Remote Sensing III
Jacky Desachy, Editor(s)

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